A CNN-Based Direction-of-Arrival Estimation from Bistatic Scattering Pattern
- Resource Type
- Conference
- Authors
- Zhao, Xiuyi; Chen, Kun-Shan; Yang, Ying
- Source
- 2021 CIE International Conference on Radar (Radar) Radar (Radar), 2021 CIE International Conference on. :514-518 Dec, 2021
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Computing and Processing
Fields, Waves and Electromagnetics
Geoscience
Robotics and Control Systems
Signal Processing and Analysis
Sea surface
Direction-of-arrival estimation
Radar measurements
Azimuth
Estimation
Scattering
Radar
direction-of-arrival (DOA) estimation
convolutional neural network (CNN)
speckle
sea surface scattering
- Language
- ISSN
- 2640-7736
The task of direction-of-arrival (DOA) estimation is to identify the signal source direction. Previous studies demonstrated that the strength of speckle-noise could be related to the size of the resolution cell and other factors, including wind speed and frequencies. In this paper, we consider the effect of resolution cell size in DOA estimation. The advanced integral equation model (AIEM) is applied as the forward model to simulate the radar scattering from sea surface under various observation geometries. To simulate the speckle noise, we evaluate the number of equivalent scatterers per resolution area and generate K-distributed speckle statistics. A convolutional neural network (CNN) is then utilized to establish the relationship of radar measurements with their incident directions. The experimental results show that the CNN structure could be applied to DOA estimation with satisfactory accuracy for high-resolution radar measurement.